Their recent caffiene consumption, and quality of previous night’s sleep.
Automation
Knowable, Explicitly Defined, Exceptionless
Measurement point selection method.
LPC parameter setting method.
Decision process for measuring a vowel or not.
Automation
Eliminated
Researcher experience and skill.
…oops
Reproducibility
Reproducibility
FAVE-extract
FAVE
FAVE
For each vowel token, the F1 and F2 estimates you could get for different LPC parameter settings constitute a candidate set.
Choose a winner based on its multivariate distance (based on F1, F2, log(B1), log(B2)) to the Atlas of North American English’s distribution for that vowel class.
Logic: If there is an LPC setting whch is produces a measurement close to the ANAE distribution for that vowel class, it’s probably ok.
FAVE - Once more, but Bayesian this time
The ANAE distribution for a vowel class is the prior.
The candidate set of potential formant estimates is the likelihood.
The winner is the posterior.
Like most worries about Bayesian reasoning, people worry that the prior might exert too strong an influence on the posterior.
Fortunately, the prior’s influence here doesn’t seem to be too strong.